Data Analysis Flyby: Selected Highlights from the Past Week

Last week, the spacecraft New Horizons flew by Pluto and took high resolution photos of the dwarf planet and its moons, giving a more detailed view of Pluto’s landscape, and revealing the presence of icy mountains as tall as the Rockies and a distinctive shape on the surface of the planet that is being called “the heart.”

Pluto is dominated by the feature informally called “the heart.”  Photo credit: NASA

In that spirit, I’d like to make today’s post a quick flyby of some of the interesting and useful articles and blog entries about data analysis that I’ve encountered over the past week.

From the perspective of better understanding the landscape of data analysis, I particularly enjoyed a post at the Abbot Analytics blog entitled “Data Mining’s Forgotten Step-Children.”  The post points out that two types of data mining get most of the attention.  The most talked-about by far is predictive modeling, also known as “supervised learning.”  The second most popular is clustering: “Despite being second banana to prediction, clustering enjoys widespread application and is well understood even in non-technical circles. What marketer doesn’t like a good segmentation?”

So, then, what are the “forgotten step-children”?  As the post explains, they include anomaly detection, association rule discovery, and data visualization.  From my perspective, as someone who has long been interested in data analysis but is still a relative novice, it is good to keep all of these in mind, as I learn more about different ways of working with and asking questions of data.

On that note, there is a helpful article at the Harvard Business Review called “Dispel Your Team’s Fear of Data”  by Thomas C. Redman.  He makes the point that many people have had bad experiences with data in the past, and that, partly because of those experiences, it was easy for many people to ignore data and analytics, but that has been changing.  Now more and more managers and their teams need to engage with data and to understand it, but before that can happen, they need to overcome their fear of data.

Redman suggests a few recent books that he finds useful and informative which might help people gain a greater appreciation for and understanding of working with data.  But what I really liked about his article was that it overlapped with some of the lessons from the “data storyteller” that I wrote about a week ago.  Redman suggests that people should practice finding more ways to work with data that interests them:

Then, find ways to practice using data. Pick something that interests you, such as whether meetings start on time, your commute time, or your fitness regimen, and gather some data, recording it on paper or electronically. Create some simple plots (such as a time-series plot) and compute some statistics (such as the average and the range). Ask yourself what the data means and explore its implications.

Finally, a third post guest-written by Matthew Scharpnick at Beth Kanter’s blog was noteworthy for relating the idea of “data storytelling” with one of the “step-children,” data visualization, and then tying both back to the nonprofit sector.  It is entitled “Five Tips for Nonprofit Data Storytelling.”  I recommend reading the post for all of the details, but the five tips boil down to: 1). Context is king, 2). Avoid unnecessary distractions, 3). Labels matter, 4). Strive for surprises, and 5). Be honest.

Did you come across any particularly interesting or noteworthy articles or blog entries about data analysis during the past week?  If so, please share them in the comments below.

And if you haven’t already done so, look for me on Twitter for more items of interest throughout the week.


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